Merge branch 'main' into feature/working-patterns-and-flexibility-score

.Rbuildignore

@@ -16,5 +16,9 @@
 # Ignore all figures
 ^man/figures
 
-# Ignore all vignettes
-^vignettes
+# Ignore vignettes
+^vignettes/best-practices.Rmd$
+^vignettes/Change-over-time.Rmd$
+^vignettes/IV-report.Rmd$
+^vignettes/text-mining-capabilities.Rmd$
+^Meta$

DESCRIPTION

@@ -23,19 +23,17 @@ Description: Opinionated functions that enable easier and faster
     This package adheres to tidyverse principles and works well with the pipe syntax.
     wpa is built with the beginner-to-intermediate R users in mind, and is optimised for
     simplicity. 
-URL: https://github.com/microsoft/wpa
-BugReports: https://github.com/microsoft/wpa/issues
+URL: https://github.com/microsoft/wpa/
+BugReports: https://github.com/microsoft/wpa/issues/
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
 Depends:
     R (>= 3.1.2)
 Imports: 
-    base,
     dplyr,
     stats,
     utils,
-    tibble,
     tidyr,
     tidyselect (>= 1.0.0),
     magrittr,
@@ -46,7 +44,6 @@ Imports:
     Information,
     scales,
     htmltools,
-    knitr,
     markdown,
     rmarkdown,
     networkD3,
@@ -55,15 +52,15 @@ Imports:
     ggraph,
     igraph,
     proxy,
-    rlang,
     ggwordcloud,
     methods,
-    data.table,
-    lifecycle
+    data.table
 RoxygenNote: 7.1.1
 Roxygen: list(markdown = TRUE)
 VignetteBuilder: knitr
 Suggests: 
+    knitr,
     extrafont,
-    leiden
+    leiden,
+    lifecycle
 Language: en-US

NAMESPACE

@@ -186,8 +186,6 @@ importFrom(proxy,dist)
 importFrom(purrr,map_if)
 importFrom(purrr,pmap)
 importFrom(purrr,reduce)
-importFrom(rlang,"!!")
-importFrom(rlang,sym)
 importFrom(scales,percent)
 importFrom(scales,wrap_format)
 importFrom(stats,cutree)

R/network_describe.R

@@ -8,7 +8,7 @@
 #' @author Tannaz Sattari Tabrizi <Tannaz.Sattari@microsoft.com>
 #'
 #' @description
-#' \Sexpr[results=rd]{lifecycle::badge("experimental")}
+#' `r lifecycle::badge('experimental')`
 #' Returns a data frame that gives a percentage of the group combinations that best represent
 #' the population provided. Uses a person to person query.
 #'

R/network_leiden.R

@@ -6,7 +6,7 @@
 #' @title Implement the Leiden community detection on a Person to Person network query
 #'
 #' @description
-#' \Sexpr[results=rd]{lifecycle::badge("experimental")}
+#' `r lifecycle::badge('experimental')`
 #' Take a P2P network query and implement the Leiden community detection method. To run
 #' this function, you will require all the pre-requisites of the **leiden** package installed,
 #' which includes Python and **reticulate**.

R/network_louvain.R

@@ -6,7 +6,7 @@
 #' @title Implement the Louvain community detection on a Person to Person network query
 #'
 #' @description
-#' \Sexpr[results=rd]{lifecycle::badge("experimental")}
+#' `r lifecycle::badge('experimental')`
 #' Take a P2P network query and implement the Louvain community detection method. The
 #' **igraph** implementation of the Louvain method is used.
 #'

R/network_p2p.R

@@ -6,7 +6,7 @@
 #' @title Create a network plot with the person-to-person query
 #'
 #' @description
-#' \Sexpr[results=rd]{lifecycle::badge("experimental")}
+#' `r lifecycle::badge('experimental')`
 #' Pass a data frame containing a person-to-person query and return a network visualization.
 #' Options are available for community detection using either the Louvain or the Leiden algorithms.
 #'

R/period_change.R

@@ -31,6 +31,7 @@
 #' ## Run plot
 #' period_change(sq_data, compvar = "Workweek_span", before_end = "2019-11-16")
 #'
+#' \donttest{
 #' ## Run plot with more specific arguments
 #' period_change(sq_data,
 #'               compvar = "Workweek_span",
@@ -39,7 +40,7 @@
 #'               after_start = "2019-12-03",
 #'               after_end = "2019-12-16",
 #'               return = "percentage")
-#'
+#' }
 #' @family Flexible Input
 #' @export
 

R/personas_hclust.R

@@ -6,7 +6,7 @@
 #' @title Create hierarchical clusters of selected metrics using a Person query
 #'
 #' @description
-#' \Sexpr[results=rd]{lifecycle::badge("questioning")}
+#' `r lifecycle::badge('questioning')`
 #' Apply hierarchical clustering to selected metrics. Person averages are computed prior to clustering.
 #' The hierarchical clustering uses cosine distance and the ward.D method
 #' of agglomeration.
@@ -43,12 +43,13 @@
 #'                 k = 4,
 #'                 return = "table")
 #'
+#' \donttest{
 #' ## Return data with clusters appended
 #' personas_hclust(sq_data,
 #'                 metrics = c("Collaboration_hours", "Workweek_span"),
 #'                 k = 4,
 #'                 return = "data")
-#'
+#' }
 #'
 #' @family Work Patterns
 #'

R/remove_outliers.R

@@ -5,7 +5,9 @@
 
 #' @title Remove outliers from a person query across time
 #'
-#' @description This function takes in a selected metric and uses z-score (number of standard
+#' @description
+#' `r lifecycle::badge('experimental')`
+#' This function takes in a selected metric and uses z-score (number of standard
 #' deviations) to identify and remove outlier weeks for individuals across time. There are applications
 #' in this for removing weeks with abnormally low collaboration
 #' activity, e.g. holidays. Retains metrics with z > -2.
@@ -15,7 +17,6 @@
 #' has a longer runtime compared to `identify_outlier()`.
 #'
 #' @details
-#' This function is on an experimental lifecycle.
 #' For mature functions to remove common outliers, please see the following:
 #' - `identify_holidayweeks()`
 #' - `identify_nkw()`

R/tm_clean.R

@@ -48,7 +48,7 @@ tm_clean <- function(data,
   # Remove WPI custom irrelevant words
   if(!is.data.frame(stopwords)){
 
-    stopwords <- tibble::tibble(word = "")
+    stopwords <- dplyr::tibble(word = "")
 
   }
 

R/workpatterns_hclust.R

@@ -242,8 +242,6 @@ workpatterns_hclust <- function(data,
 #'
 #' @import ggplot2
 #' @import dplyr
-#' @importFrom rlang sym
-#' @importFrom rlang "!!"
 #' @importFrom tidyselect all_of
 #' @importFrom tidyr gather
 #'
@@ -387,7 +385,7 @@ run_sum_hr <- function(data,
       mode_val <- uni[which.max(tabulate(match(hour_raw, uni)))]
 
 
-      tibble::tibble(cluster = x$cluster[1],
+      dplyr::tibble(cluster = x$cluster[1],
                      median_hour = median(hour_raw),
                      p5_hour = stats::quantile(hour_raw, .05),
                      p25_hour = stats::quantile(hour_raw, .25),

README.md

@@ -1,10 +1,10 @@
-# wpa <img src="man/figures/logo2.png" align="right" width=15% />
+# wpa <img src="https://raw.githubusercontent.com/microsoft/wpa/main/man/figures/logo2.png" align="right" width=15% />
 
-  [![R build status](https://github.com/microsoft/wpa/workflows/R-CMD-check/badge.svg)](https://github.com/microsoft/wpa/actions) [![CodeFactor](https://www.codefactor.io/repository/github/microsoft/wpa/badge)](https://www.codefactor.io/repository/github/microsoft/wpa) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)
+  [![R build status](https://github.com/microsoft/wpa/workflows/R-CMD-check/badge.svg)](https://github.com/microsoft/wpa/actions/) [![CodeFactor](https://www.codefactor.io/repository/github/microsoft/wpa/badge/)](https://www.codefactor.io/repository/github/microsoft/wpa/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT/) [![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)
 
 ## Analyze and Visualize Workplace Analytics data
 
-This is an R package for analyzing and visualizing data from [Microsoft Workplace Analytics](https://www.microsoft.com/microsoft-365/partners/workplaceanalytics).
+This is an R package for analyzing and visualizing data from [Microsoft Workplace Analytics](https://docs.microsoft.com/en-us/workplace-analytics/).
 
 ## With the **wpa** package, you can...
 
@@ -18,7 +18,7 @@ This is an R package for analyzing and visualizing data from [Microsoft Workplac
 
 Here is an example of **wpa** in action:
 
-<img src="man/figures/output2.gif" align="center" width=80% />
+<img src="https://raw.githubusercontent.com/microsoft/wpa/main/man/figures/output2.gif" align="center" width=80% />
 
 ---
 
@@ -28,6 +28,7 @@ To get started with the package,  please see the following links:
 
 - [Installation and Set-up](https://microsoft.github.io/wpa/analyst_guide_getting_started.html)
 - [Full function list](https://microsoft.github.io/wpa/reference/index.html)
+- [Analyst Guide](https://microsoft.github.io/wpa/analyst_guide.html)
 
 Also check out our package cheat sheet for a quick glimpse of what **wpa** offers:
 
@@ -40,27 +41,27 @@ Also check out our package cheat sheet for a quick glimpse of what **wpa** offer
 We welcome contributions to the package! 
 
 ### Contributing code
-If you would like contribute code to the repo, please read our [Contributor Guide](CONTRIBUTING.md) and [Developer Guide](.github/developer_guide.md). This documentation should provide you all the information you will need to get started.
+If you would like contribute code to the repo, please read our [Contributor Guide](https://microsoft.github.io/wpa/CONTRIBUTING.html) and [Developer Guide](https://microsoft.github.io/wpa/developer_guide.html). This documentation should provide you all the information you will need to get started.
 
 ### Issues or Feature Requests
 If you would like to log an issue or submit a feature request, please create a new issue or comment on an existing issue on [GitHub Issues](https://github.com/microsoft/wpa/issues) on this repo.
 
 ### Reporting Security Issues
-Please do not report security vulnerabilities through public GitHub issues. Please read our Security document [for more details](.github/reporting_security_issues.md).
+Please do not report security vulnerabilities through public GitHub issues. Please read our Security document [for more details](https://microsoft.github.io/wpa/reporting_security_issues.html).
 
 ### Changelog
-See [NEWS.md](NEWS.md) for the package changelog.
+See [NEWS.md](https://microsoft.github.io/wpa/news/index.html) for the package changelog.
 
 ---
 
 ## Code of Conduct
 
-We would ask you to please read the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct) prior to engaging with this package.
+We would ask you to please read the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) prior to engaging with this package.
 
 
 **Trademarks** 
 
-This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
+This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general/). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
 
 ---
 

man/network_describe.Rd

@@ -15,7 +15,7 @@ network_describe(
 \item{hrvar}{Character vector of length 3 containing the HR attributes to be used.}
 }
 \description{
-\Sexpr[results=rd]{lifecycle::badge("experimental")}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 Returns a data frame that gives a percentage of the group combinations that best represent
 the population provided. Uses a person to person query.
 }

man/network_leiden.Rd

@@ -62,7 +62,7 @@ Set as \code{0} to co-erce to a fast plotting method every time, and \code{Inf}
 method.}
 }
 \description{
-\Sexpr[results=rd]{lifecycle::badge("experimental")}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 Take a P2P network query and implement the Leiden community detection method. To run
 this function, you will require all the pre-requisites of the \strong{leiden} package installed,
 which includes Python and \strong{reticulate}.

man/network_louvain.Rd

@@ -55,7 +55,7 @@ Set as \code{0} to co-erce to a fast plotting method every time, and \code{Inf}
 method.}
 }
 \description{
-\Sexpr[results=rd]{lifecycle::badge("experimental")}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 Take a P2P network query and implement the Louvain community detection method. The
 \strong{igraph} implementation of the Louvain method is used.
 }

man/network_p2p.Rd

@@ -81,7 +81,7 @@ Set as \code{0} to co-erce to a fast plotting method every time, and \code{Inf}
 method.}
 }
 \description{
-\Sexpr[results=rd]{lifecycle::badge("experimental")}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 Pass a data frame containing a person-to-person query and return a network visualization.
 Options are available for community detection using either the Louvain or the Leiden algorithms.
 }

man/period_change.Rd

@@ -41,6 +41,7 @@ that the variable has not changed, using a Wilcox signed-rank test.
 ## Run plot
 period_change(sq_data, compvar = "Workweek_span", before_end = "2019-11-16")
 
+\donttest{
 ## Run plot with more specific arguments
 period_change(sq_data,
               compvar = "Workweek_span",
@@ -49,7 +50,7 @@ period_change(sq_data,
               after_start = "2019-12-03",
               after_end = "2019-12-16",
               return = "percentage")
-
+}
 }
 \seealso{
 Other Flexible Input: 

man/personas_hclust.Rd

@@ -23,7 +23,7 @@ Valid options include:
 }}
 }
 \description{
-\Sexpr[results=rd]{lifecycle::badge("questioning")}
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#questioning'><img src='figures/lifecycle-questioning.svg' alt='Questioning lifecycle'></a>}}{\strong{Questioning}}
 Apply hierarchical clustering to selected metrics. Person averages are computed prior to clustering.
 The hierarchical clustering uses cosine distance and the ward.D method
 of agglomeration.
@@ -40,12 +40,13 @@ personas_hclust(sq_data,
                 k = 4,
                 return = "table")
 
+\donttest{
 ## Return data with clusters appended
 personas_hclust(sq_data,
                 metrics = c("Collaboration_hours", "Workweek_span"),
                 k = 4,
                 return = "data")
-
+}
 
 }
 \seealso{

man/remove_outliers.Rd

@@ -18,6 +18,7 @@ having removed the person-weeks that are below 2 standard
 deviations of each individual's collaboration activity.
 }
 \description{
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
 This function takes in a selected metric and uses z-score (number of standard
 deviations) to identify and remove outlier weeks for individuals across time. There are applications
 in this for removing weeks with abnormally low collaboration
@@ -28,7 +29,6 @@ identified and removed \strong{with respect to each individual}, as opposed to t
 has a longer runtime compared to \code{identify_outlier()}.
 }
 \details{
-This function is on an experimental lifecycle.
 For mature functions to remove common outliers, please see the following:
 \itemize{
 \item \code{identify_holidayweeks()}

vignettes/intro-to-wpa.Rmd

@@ -20,17 +20,15 @@ knitr::opts_chunk$set(
 
 ## Background
 
-This document walks through the **wpa** package, and provides some examples on how to use some of the functions. For our full online documentation for the package, please visit <https://microsoft.github.io/wpa>. For anything else related to Workplace Analytics, please visit <https://docs.microsoft.com/en-us/workplace-analytics/>.
+This document walks through the **wpa** package, and provides some examples on how to use some of the functions. For our full online documentation for the package, please visit <https://microsoft.github.io/wpa/>. For anything else related to Workplace Analytics, please visit <https://docs.microsoft.com/en-us/workplace-analytics/>.
 
 ### Setting up
 
-To start off using **wpa**, you'll have to load it by running `library(wpa)`. For the purpose of our examples, let's also loada few of the component packages of **tidyverse** (alternatively, you can just run `library(tidyverse)`:
+To start off using **wpa**, you'll have to load it by running `library(wpa)`. For the purpose of our examples, let's also load **dplyr** as a component package of **tidyverse** (alternatively, you can just run `library(tidyverse)`:
 
 ```{r message=FALSE, warning=FALSE}
 library(wpa)
 library(dplyr)
-library(tibble)
-library(tidyr)
 ```
 
 The package ships with a standard Person query dataset `sq_data`:
@@ -202,4 +200,4 @@ Running the function will open up a HTML report in your browser, summarising the
 
 ## Feedback
 
-Hope you found this useful! If you have any suggestions or feedback, please log them at <https://github.com/microsoft/wpa/issues>.
+Hope you found this useful! If you have any suggestions or feedback, please log them at <https://github.com/microsoft/wpa/issues/>.